scholarly journals RUPEE: A fast and accurate purely geometric protein structure search

2018 ◽  
Author(s):  
Ronald Ayoub ◽  
Yugyung Lee
Keyword(s):  
Big Data ◽  
Protein Structure ◽  
Response Times ◽  
Protein Structures ◽  
Fast Response ◽  
Link Type ◽  
Novel Approach ◽  
Structure Search ◽  
Close Relationship ◽  
And Function

AbstractGiven the close relationship between protein structure and function, protein structure searches have long played an established role in bioinformatics. Despite their maturity, existing protein structure searches either use simplifying assumptions or compromise between fast response times and quality of results. These limitations can prevent the easy and efficient exploration of relationships between protein structures, which is the norm in other areas of inquiry. We have developed RUPEE, a fast, scalable, and purely geometric structure search combining techniques from information retrieval and big data with a novel approach to encoding sequences of torsion angles.Comparing our results to the output of mTM, SSM, and the CATHEDRAL structural scan, it is clear that RUPEE has set a new bar for purely geometric big data approaches to protein structure searches. RUPEE in top-aligned mode produces equal or better results than the best available protein structure searches, and RUPEE in fast mode demonstrates the fastest response times coupled with high quality results.The RUPEE protein structure search is available at http://www.ayoubresearch.com. Code and data are available at https://github.com/rayoub/rupee.

scholarly journals Protein structure search to support the development of protein structure prediction methods

2020 ◽  
Author(s):  
Ronald Ayoub ◽  
Yugyung Lee
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Unsolved Problem ◽  
Protein Structures ◽  
Prediction Methods ◽  
Link Type ◽  
Recent Success ◽  
Structure Search ◽  
Structure Similarity

AbstractProtein structure prediction is a long-standing unsolved problem in molecular biology that has seen renewed interest with the recent success of deep learning with AlphaFold at CASP13. While developing and evaluating protein structure prediction methods, researchers may want to identify the most similar known structures to their predicted structures. These predicted structures often have low sequence and structure similarity to known structures. We show how RUPEE, a purely geometric protein structure search, is able to identify the structures most similar to structure predictions, regardless of how they vary from known structures, something existing protein structure searches struggle with. RUPEE accomplishes this through the use of a novel linear encoding of protein structures as a sequence of residue descriptors. Using a fast Needleman-Wunsch algorithm, RUPEE is able to perform alignments on the sequences of residue descriptors for every available structure. This is followed by a series of increasingly accurate structure alignments from TM-align alignments initialized with the Needleman-Wunsch residue descriptor alignments to standard TM-align alignments of the final results. By using alignment normalization effectively at each stage, RUPEE also can execute containment searches in addition to full-length searches to identify structural motifs within proteins. We compare the results of RUPEE to mTM-align, SSM, CATHEDRAL and VAST using a benchmark derived from the protein structure predictions submitted to CASP13. RUPEE identifies better alignments on average with respect to RMSD and TM-score as well as Q-score and SSAP-score, scores specific to SSM and CATHEDRAL, respectively. Finally, we show a sample of the top-scoring alignments that RUPEE identified that none of the other protein structure searches we compared to were able to identify.The RUPEE protein structure search is available at https://ayoubresearch.com. Code and data are available at https://github.com/rayoub/rupee.

3. Proteins

2021 ◽  
pp. 34-51
Author(s):  
Mark Lorch
Keyword(s):  
Amino Acids ◽  
Protein Folding ◽  
Protein Structure ◽  
Protein Structures ◽  
Structure And Function ◽  
Vast Array ◽  
A Cell ◽  
Cellular Machinery ◽  
And Function ◽  
The Relationship

This chapter examines proteins, the dominant proportion of cellular machinery, and the relationship between protein structure and function. The multitude of biological processes needed to keep cells functioning are managed in the organism or cell by a massive cohort of proteins, together known as the proteome. The twenty amino acids that make up the bulk of proteins produce the vast array of protein structures. However, amino acids alone do not provide quite enough chemical variety to complete all of the biochemical activity of a cell, so the chapter also explores post-translation modifications. It finishes by looking as some dynamic aspects of proteins, including enzyme kinetics and the protein folding problem.

scholarly journals A New Clustering and Nomenclature for Beta Turns Derived from High-Resolution Protein Structures

2018 ◽  
Author(s):  
Maxim Shapovalov ◽  
Slobodan Vucetic ◽  
Roland L. Dunbrack
Keyword(s):  
Protein Structure ◽  
High Resolution ◽  
Protein Structures ◽  
Protein Structure Determination ◽  
Classical Type ◽  
Type I ◽  
Beta Turns ◽  
Data Set ◽  
Beta Turn ◽  
Link Type

AbstractProtein loops connect regular secondary structures and contain 4-residue beta turns which represent 63% of the residues in loops. The commonly used classification of beta turns (Type I, I’, II, II’, VIa1, VIa2, VIb, and VIII) was developed in the 1970s and 1980s from analysis of a small number of proteins of average resolution, and represents only two thirds of beta turns observed in proteins (with a generic class Type IV representing the rest). We present a new clustering of beta turn conformations from a set of 13,030 turns from 1078 ultra-high resolution protein structures (≤1.2 Å). Our clustering is derived from applying the DBSCAN andk-medoids algorithms to this data set with a metric commonly used in directional statistics applied to the set of dihedral angles from the second and third residues of each turn. We define 18 turn types compared to the 8 classical turn types in common use. We propose a new 2-letter nomenclature for all 18 beta-turn types using Ramachandran region names for the two central residues (e.g., ‘A’ and ‘D’ for alpha regions on the left side of the Ramachandran map and ‘a’ and ‘d’ for equivalent regions on the right-hand side; classical Type I turns are ‘AD’ turns and Type I’ turns are ‘ad’). We identify 11 new types of beta turn, 5 of which are sub-types of classical beta turn types. Up-to-date statistics, probability densities of conformations, and sequence profiles of beta turns in loops were collected and analyzed. A library of turn types,BetaTurnLib18, and cross-platform software,BetaTurnTool18, which identifies turns in an input protein structure, are freely available and redistributable fromdunbrack.fccc.edu/betaturnandgithub.com/sh-maxim/BetaTurn18. Given the ubiquitous nature of beta turns, this comprehensive study updates understanding of beta turns and should also provide useful tools for protein structure determination, refinement, and prediction programs.

scholarly journals Solving a new R2lox protein structure by microcrystal electron diffraction

2019 ◽  
Vol 5 (8) ◽  
pp. eaax4621 ◽  
Author(s):  
Hongyi Xu ◽  
Hugo Lebrette ◽  
Max T. B. Clabbers ◽  
Jingjing Zhao ◽  
Julia J. Griese ◽  
...  
Keyword(s):  
Protein Structure ◽  
Electron Diffraction ◽  
Model Building ◽  
Protein Structures ◽  
X Ray Diffraction ◽  
X Ray ◽  
X Ray Crystallography ◽  
Potential Map ◽  
Metal Cofactor ◽  
And Function

Microcrystal electron diffraction (MicroED) has recently shown potential for structural biology. It enables the study of biomolecules from micrometer-sized 3D crystals that are too small to be studied by conventional x-ray crystallography. However, to date, MicroED has only been applied to redetermine protein structures that had already been solved previously by x-ray diffraction. Here, we present the first new protein structure—an R2lox enzyme—solved using MicroED. The structure was phased by molecular replacement using a search model of 35% sequence identity. The resulting electrostatic scattering potential map at 3.0-Å resolution was of sufficient quality to allow accurate model building and refinement. The dinuclear metal cofactor could be located in the map and was modeled as a heterodinuclear Mn/Fe center based on previous studies. Our results demonstrate that MicroED has the potential to become a widely applicable tool for revealing novel insights into protein structure and function.

scholarly journals FASPR: an open-source tool for fast and accurate protein side-chain packing

2020 ◽  
Vol 36 (12) ◽  
pp. 3758-3765 ◽  
Author(s):  
Xiaoqiang Huang ◽  
Robin Pearce ◽  
Yang Zhang
Keyword(s):  
Protein Structure ◽  
Protein Design ◽  
Structure Prediction ◽  
Protein Structures ◽  
Scoring Function ◽  
Supplementary Information ◽  
Side Chain ◽  
Chain Packing ◽  
And Function ◽  
Side Chain Packing

Abstract Motivation Protein structure and function are essentially determined by how the side-chain atoms interact with each other. Thus, accurate protein side-chain packing (PSCP) is a critical step toward protein structure prediction and protein design. Despite the importance of the problem, however, the accuracy and speed of current PSCP programs are still not satisfactory. Results We present FASPR for fast and accurate PSCP by using an optimized scoring function in combination with a deterministic searching algorithm. The performance of FASPR was compared with four state-of-the-art PSCP methods (CISRR, RASP, SCATD and SCWRL4) on both native and non-native protein backbones. For the assessment on native backbones, FASPR achieved a good performance by correctly predicting 69.1% of all the side-chain dihedral angles using a stringent tolerance criterion of 20°, compared favorably with SCWRL4, CISRR, RASP and SCATD which successfully predicted 68.8%, 68.6%, 67.8% and 61.7%, respectively. Additionally, FASPR achieved the highest speed for packing the 379 test protein structures in only 34.3 s, which was significantly faster than the control methods. For the assessment on non-native backbones, FASPR showed an equivalent or better performance on I-TASSER predicted backbones and the backbones perturbed from experimental structures. Detailed analyses showed that the major advantage of FASPR lies in the optimal combination of the dead-end elimination and tree decomposition with a well optimized scoring function, which makes FASPR of practical use for both protein structure modeling and protein design studies. Availability and implementation The web server, source code and datasets are freely available at https://zhanglab.ccmb.med.umich.edu/FASPR and https://github.com/tommyhuangthu/FASPR. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MISCAST: MIssense variant to protein StruCture Analysis web SuiTe

2020 ◽  
Vol 48 (W1) ◽  
pp. W132-W139
Author(s):  
Sumaiya Iqbal ◽  
David Hoksza ◽  
Eduardo Pérez-Palma ◽  
Patrick May ◽  
Jakob B Jespersen ◽  
...  
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
Structure Analysis ◽  
Protein Structures ◽  
Missense Variant ◽  
Protein Structure Analysis ◽  
Missense Variants ◽  
Functional Features ◽  
And Function ◽  
A Site

Abstract Human genome sequencing efforts have greatly expanded, and a plethora of missense variants identified both in patients and in the general population is now publicly accessible. Interpretation of the molecular-level effect of missense variants, however, remains challenging and requires a particular investigation of amino acid substitutions in the context of protein structure and function. Answers to questions like ‘Is a variant perturbing a site involved in key macromolecular interactions and/or cellular signaling?’, or ‘Is a variant changing an amino acid located at the protein core or part of a cluster of known pathogenic mutations in 3D?’ are crucial. Motivated by these needs, we developed MISCAST (missense variant to protein structure analysis web suite; http://miscast.broadinstitute.org/). MISCAST is an interactive and user-friendly web server to visualize and analyze missense variants in protein sequence and structure space. Additionally, a comprehensive set of protein structural and functional features have been aggregated in MISCAST from multiple databases, and displayed on structures alongside the variants to provide users with the biological context of the variant location in an integrated platform. We further made the annotated data and protein structures readily downloadable from MISCAST to foster advanced offline analysis of missense variants by a wide biological community.

scholarly journals Solving the first novel protein structure by 3D micro-crystal electron diffraction

2019 ◽  
Author(s):  
H. Xu ◽  
H. Lebrette ◽  
M.T.B. Clabbers ◽  
J. Zhao ◽  
J.J. Griese ◽  
...  
Keyword(s):  
Protein Structure ◽  
Electron Diffraction ◽  
Model Building ◽  
Protein Structures ◽  
X Ray ◽  
X Ray Crystallography ◽  
Unknown Protein ◽  
Potential Map ◽  
Crystal Electron ◽  
And Function

AbstractMicro-crystal electron diffraction (MicroED) has recently shown potential for structural biology. It enables studying biomolecules from micron-sized 3D crystals that are too small to be studied by conventional X-ray crystallography. However, to the best of our knowledge, MicroED has only been applied to re-determine protein structures that had already been solved previously by X-ray diffraction. Here we present the first unknown protein structure – an R2lox enzyme – solved using MicroED. The structure was phased by molecular replacement using a search model of 35% sequence identity. The resulting electrostatic scattering potential map at 3.0 Å resolution was of sufficient quality to allow accurate model building and refinement. Our results demonstrate that MicroED has the potential to become a widely applicable tool for revealing novel insights into protein structure and function, opening up new opportunities for structural biologists.

scholarly journals CoRINs: A tool to compare residue interaction networks from homologous proteins and conformers

2020 ◽  
Author(s):  
Felipe V. da Fonseca ◽  
Romildo O. Souza Júnior ◽  
Marília V. A. de Almeida ◽  
Thiago D. Soares ◽  
Diego A. A. Morais ◽  
...  
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
Conformational Changes ◽  
Protein Function ◽  
Protein Structures ◽  
Software Tool ◽  
Interaction Networks ◽  
Homologous Proteins ◽  
Residue Interaction ◽  
And Function

ABSTRACTMotivationA useful approach to evaluate protein structure and quickly visualize crucial physicochemical interactions related to protein function is to construct Residue Interactions Networks (RINs). By using this application of graphs theory, the amino acid residues constitute the nodes, and the edges represent their interactions with other structural elements. Although several tools that construct RINs are available, many of them do not compare RINs from distinct protein structures. This comparison can give valuable insights into the understanding of conformational changes and the effects of amino acid substitutions in protein structure and function. With that in mind, we present CoRINs (Comparator of Residue Interaction Networks), a software tool that extensively compares RINs. The program has an accessible and user-friendly web interface, which summarizes the differences in several network parameters using interactive plots and tables. As a usage example of CoRINs, we compared RINs from conformers of two cancer-associated proteins.AvailabilityThe program is available at https://github.com/LasisUFRN/CoRINs.

scholarly journals Learning structural motif representations for efficient protein structure search

2017 ◽  
Author(s):  
Yang Liu ◽  
Qing Ye ◽  
Liwei Wang ◽  
Jian Peng
Keyword(s):  
Protein Structure ◽  
Fundamental Problem ◽  
Sequence Similarity ◽  
Structural Alignment ◽  
Protein Structures ◽  
Computational Cost ◽  
Data Bank ◽  
Hierarchical Organization ◽  
Structural Motif ◽  
And Function

AbstractMotivationUnderstanding the relationship between protein structure and function is a fundamental problem in protein science. Given a protein of unknown function, fast identification of similar protein structures from the Protein Data Bank (PDB) is a critical step for inferring its biological function. Such structural neighbors can provide evolutionary insights into protein conformation, interfaces and binding sites that are not detectable from sequence similarity. However, the computational cost of performing pairwise structural alignment against all structures in PDB is prohibitively expensive. Alignment-free approaches have been introduced to enable fast but coarse comparisons by representing each protein as a vector of structure features or fingerprints and only computing similarity between vectors. As a notable example, FragBag represents each protein by a “bag of fragments”, which is a vector of frequencies of contiguous short backbone fragments from a predetermined library.ResultsHere we present a new approach to learning effective structural motif presentations using deep learning. We develop DeepFold, a deep convolutional neural network model to extract structural motif features of a protein structure. Similar to FragBag, DeepFold represents each protein structure or fold using a vector of learned structural motif features. We demonstrate that DeepFold substantially outperforms FragBag on protein structural search on a non-redundant protein structure database and a set of newly released structures. Remarkably, DeepFold not only extracts meaningful backbone segments but also finds important long-range interacting motifs for structural comparison. We expect that DeepFold will provide new insights into the evolution and hierarchical organization of protein structural motifs.Availabilityhttps://github.com/largelymfs/[email protected]

scholarly journals Determining protein structures using genetics

2018 ◽  
Author(s):  
Jörn M. Schmiedel ◽  
Ben Lehner
Keyword(s):  
Structure Determination ◽  
Low Cost ◽  
Protein Structures ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Biological Research ◽  
X Ray Crystallography ◽  
Close Relationship ◽  
And Function

SummaryDetermining the three dimensional structures of macromolecules is a major goal of biological research because of the close relationship between structure and function. Structure determination usually relies on physical techniques including x-ray crystallography, NMR spectroscopy and cryo-electron microscopy. Here we present a method that allows the high-resolution three-dimensional structure of a biological macromolecule to be determined only from measurements of the activity of mutant variants of the molecule. This genetic approach to structure determination relies on the quantification of genetic interactions (epistasis) between mutations and the discrimination of direct from indirect interactions. This provides a new experimental strategy for structure determination, with the potential to reveal functional and in vivo structural conformations at low cost and high throughput.

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